By running simulation tests in Pega Customer Decision Hub, you can derive useful intelligence that can help you make important business decisions. For example, you can examine the effect of a new product offer or assess risk in a variety of marketing or nonmarketing scenarios.
With simulation tests, you can run strategies of varying complexity on a preselected sample set of customers. By doing so, you can make millions of decisions at the same time and simulate the outcome of your decision management framework. After a simulation test has been completed, you can visualize the results in Scenario Planner, where you can check whether the new strategy produces the expected result. For example, you can check whether customers are offered a new phone or Internet plan when certain conditions that are specified in the strategy are met. You can also assess the effect of the new product on your existing product offering.
In Pega Customer Decision Hub, you can run simulation tests with minimal amount of configuration. For example, you do not have to configure any simulation data flows or data set where the simulation result will be stored.
Additionally, you can perform various operations on already completed simulation tests, such as assigning additional reports to a simulation test or comparing simulation tests in Scenario Planner. You can also schedule a simulation test to run in the future. To evaluate your new strategies on the spot, you can simulate strategies directly from the Strategy canvas.
Understanding the simulation flow
Simulations progress through the following stages in Pega Customer Decision Hub:
- Setup - In this stage, the simulation settings are configured.
- Execute - In this stage, the simulation runs.
- Review - In this stage, the simulation results are available for your review.
- Resolve - This stage signifies that the simulation is completed and the results have been reviewed.
Simulation tests and the decision funnel
You can use simulation tests to understand the decision funnel, that is, the effect of the components that influence the outcome of a decision. Decision funnel explanation simulations break down the results of a decision funnel into granular analyses that help you understand how certain components of a decision influence the overall outcome. This type of simulation test includes predefined reports that drill down to the details of proposition counts by proposition filter, prioritization, switch and champion-challenger components.
To view the proposition count breakdown for a decision, select Decision funnel when you create a simulation.
Using simulation tests to discover unwanted biasYou can run simulation tests in order to detect unwanted bias that may be present in your strategy results. For example, you can test whether your strategies generate biased results by sending more actions to female rather than male customers.
Value Finder simulations
Run a Value Finder simulation on an audience to discover which stages of your next-best-action strategy leave customers without any actions or with only low propensity actions, and identify groups of customers that are underserved.
Simulation tests and revision managementIf the environment where you want to simulate strategies is enabled for revision management, you can run simulation tests in the context of a specific revision. In revision management, simulation outputs are created as part of enterprise application rulesets, instead of revision branches or revision rulesets. Therefore, simulation outputs are not available for packaging.
Additionally, you can run simulation tests for strategies that are part of unsubmitted change requests. This option is available only when you do not simulate a strategy against a specific revision.
- Sampling audiences for simulations
- decision funnel test
- Running simulation tests
- Running simulation tests from the strategy form
- Managing simulation tests in Pega Customer Decision Hub
- Testing Next-Best-Action configuration with audience simulations
- Complying with policies or regulations by detecting unwanted bias